Moving Object Databases Research Articles

Plain move predicate and its consistency concerning the moving agents in a network

Advances in GIS and databases for dealing with spatiotemporal frameworks are leading to efficient querying, analyzing, and reasoning about moving objects/agents. However, contemporary frameworks on spatiotemporal logics are usually limited to qualitative approaches, such as the numerous studies on spatiotemporal databases that focus on observations, ignoring the intended movements of the agents. Moving object databases, on the other hand, can handle queries about the location, velocity, and time by assuming some agents to be targets. However, reasoning about the plan of moving agents, especially on the network and the achievability of such a plan, still remains a challenge. Studies on vehicle routing are often about the centralized planning of moving agents from scratch and do not deal with intended plans. Based on a plain move predicate, this paper presents a generic framework that can be used for representing and reasoning about plans of moving agents. Concepts from motion, network structure, graph theory, predicate logic, and constraint satisfaction are used to create the framework. We have also provided efficient algorithms for checking the consistency of the movement and extracting compatible plans along with some discussions on computational analysis, logical deduction, and flexibility. Finally, we have demonstrated the application of the methodology by developing NETwork-based Move Atoms Planning System (NETMAPS). The experiments show how NETMAPS can overcome an inconsistent movement plan and deliver advantageous suggestions to an executive agent.

On the Management and Analysis of Our LifeSteps

Huge volumes of location information are available nowadays due to the rapid growth of positioning devices (GPS-enabled smartphones and tablets, on-board navigation systems in vehicles, vessels and planes, smart chips for animals, etc.). In the near future, it is unavoidable that this explosion will contribute in what is called the Big Data era, raising high challenges for the data management research community. Instead of trying to manage bigger and bigger volumes of raw data, future Moving Object Database (MOD) systems need to extract and manage (the minimum necessary) semantics of movement. Such semantics can foster next-generation location-based services (LBS) and locationbased social networking (LBSN) applications, building more efficient and effective applications, while in parallel opening new research directions in the field of transportation, urban planning etc. In this article, we first present a novel model that enables the unified management of (raw GPS) trajectories and their semantic counterpart, and then we discuss challenges and solutions on the multidimensional analysis of such real-world semantic-aware mobility databases and data warehouses. Our recent experience from an interdisciplinary EU project we've been participating makes us confident that the envisioned approach will inspire the next wave of research in mobility data management and exploration field.

A Spatial Data Model for Moving Object Databases

Moving Object Databases will have significant role in Geospatial Information Systems as they allow users to model continuous movements of entities in the databases and perform spatio-temporal analysis. For representing and querying moving objects, an algebra with a comprehensive framework of User Defined Types together with a set of functions on those types is needed. Moreover, concerning real world applications, moving objects move along constrained environments like transportation networks so that an extra algebra for modeling networks is demanded, too. These algebras can be inserted in any data model if their designs are based on available standards such as Open Geospatial Consortium that provides a common model for existing DBMS’s. In this paper, we focus on extending a spatial data model for constrained moving objects. Static and moving geometries in our model are based on Open Geospatial Consortium standards. We also extend Structured Query Language for retrieving, querying, and manipulating spatio-temporal data related to moving objects as a simple and expressive query language. Finally as a proof-of-concept, we implement a generator to generate data for moving objects constrained by a transportation network. Such a generator primarily aims at traffic planning applications.

A Spatial Data Model for Moving Object Databases

Moving Object Databases will have significant role in Geospatial Information Systems as they allow users to model continuous movements of entities in the databases and perform spatio-temporal analysis. For representing and querying moving objects, and algebra with a comprehensive framework of User Defined Types together with a set of functions on those types is needed. Moreover, concerning real world applications, moving objects move along constrained environments like transportation networks so that an extra algebra for modeling networks is demanded, too. These algebras can be inserted in any data model if their designs are based on available standards such as Open Geospatial Consortium that provides a common model for existing DBMS's. In this paper, we focus on extending a spatial data model for constrained moving objects. Static and moving geometries in our model are based on Open Geospatial Consortium standards. We also extend Structured Query Language for retrieving, querying, and manipulating spatio-temporal data related to moving objects as a simple and expressive query language. Finally as a proof of concept, we implement a generator to generate data for moving objects constrained by a transportation network. Such a generator primarily aims at traffic planning applications.

A taxonomy for moving object queries in spatial databases

In the past decade, many works have focused on the development of moving object database indexing and querying. Most of those works have concentrated on the common spatial queries which are used with static objects as well. However, moving objects have different features from static objects which may lead to a variety of queries. Therefore, it is important to understand the full spectrum of moving object queries, even before starting to build an index structure for such objects. The aim of this paper is to provide a complete picture of the capabilities of moving object queries. Thus motivated, in this paper we propose a taxonomy of moving object queries, comprising five perspectives: (i) Location perspective, (ii) Motion perspective, (iii) Object perspective, (vi) Temporal perspective and (v) Patterns perspective. These give an overall view of what moving object queries are about. In this work, each perspective is described and examples are given.

Geospatial data streams: Formal framework and implementation

A spatio-temporal database manages spatio-temporal objects and supports corresponding query languages. Today, the term moving objects databases is used as a synonym for spatio-temporal databases managing spatial objects with a continuously changing geospatial location and/or extent. Recent advances in wireless communication, miniaturization of spatially enabled devices and global navigation satellite systems (GNSS) services have resulted in a large number of novel application domains. Applications in these novel domains (geo-sensor networks, moving objects tracking, real-time traffic analysis, etc.) process huge volumes of continuous data streams, i.e. data sets that are produced incrementally over time, rather than those available in full before the processing begins. Several data stream management systems (DSMSs) have been developed to manage this data. Since they are mainly based on a relational paradigm, they do not support geospatial data. Therefore, there is an urgent need for geospatial data stream management, ranging from real-time monitoring and alerting to long-term analysis of processed geospatial data. In this paper we present a formal framework consisting of data types and operations needed to support geospatial data in data streams. It can be used as a basis either for implementation of a completely new geospatial DSMS, or for extending available open source products and research prototypes. We leverage the work on abstract data types from spatio-temporal databases, present an implementation based on user-defined aggregate functions and illustrate embedding into an SQL-like language.

Partition-based range query for uncertain trajectories in road networks

Query processing for trajectory data is a hot topic in the field of moving objects databases (MODs). Most of the previous research work focused on the Euclidean space, and the uncertain trajectories are represented as sheared cylinders. However, in many applications (e.g. traffic management), the movements of moving objects (MOs) are constrained by the road network environments, which makes the previous methods ineffective. In this paper, we firstly construct an uncertain trajectory model, which is composed of a sequence of segment units with earliest arrival time and latest departure time, based on the assuming availability of a maximum speed on each road segment. Secondly, we present a partition-based uncertain trajectory index (PUTI) to facilitate the search of possible MOs within the space and time range in the road networks based on the uncertain trajectory model. It provides appropriate groups to gather segment units of trajectories according to their network distances. Finally, an efficient algorithm for range query is proposed by leveraging the index. The experiments on two datasets demonstrate that the uncertain trajectory model is effective, and PUTI also significantly outperforms the network distance based MON-tree on range query.

Group spatiotemporal pattern queries

Group spatiotemporal patterns are certain formations, in space and time, shown by groups of moving objects, such as flocks, concurrence, encounter, etc. A large number of recent applications focus on the collective behavior of moving objects, rather than the individual movements. Therefore finding such groups in moving object databases is crucial. There exist, in the literature, smart algorithms for matching some of these patterns. These solutions, however, address specific patterns and require specialized data representation and indexes. They share too little to be integrated into a single system. There is a need for a generic query method that allows users to fill in pattern descriptions, and retrieve the set of matches. In this paper, we propose generic query operators that can consistently express and match a wide range of group spatiotemporal patterns. We formally define these operators, illustrate the evaluation algorithms, and discuss the issues of their integration with moving object database (MOD) systems. These operators have been implemented in the context of Secondo MOD system, and the implementation is available online as open source. Several examples are given to showcase the expressive power of the operators. We have made available scripts that can be invoked from the Secondo interface to automatically repeat some of the experiments in this paper.

On the Support of Mobility in ORDBMS

Composition of space and mobility in a unified data framework results into Moving Object Databases (MOD). MOD management systems support storage and query processing of non-static spatial objects and provide essential operations for higher level analysis of movement data. The goal of this paper is to present Hermes MOD engine that supports the aforementioned functionality through appropriate data types and methods in Object-Relational DBMS (ORDBMS) environments. In particular, Hermes exploits on the extensibility interface of ORDBMS that already have extensions for static spatial data types and methods that follow the Open Geospatial Consortium (OGC) standard, and extends the ORDBMS by supporting time-varying geometries that change their position and/or extent in space and time dimensions, either discretely or continuously. It further extends the data definition and manipulation language of the ORDBMS with spatio-temporal semantics and functionality.

Simulating visit probability distributions within planar space-time prisms

The space-time prism is key concept in time geography and moving objects databases; it demarcates all locations that a mobile object can occupy given anchor locations and times and a maximum velocity for travel. Although the prism’s spatial and temporal extent is widely applied as a measure of accessibility and object locational uncertainty, until recently little attention has been paid to the properties of the prism interior such as the probabilities of the object visiting different locations within the prism. Better understanding of the visit probability distribution within the prism can improve theoretical understanding as well as refine the prism as a practical measure of space-time accessibility and object uncertainty. This paper presents two methods for modeling the distribution of visit probabilities within planar space-time prisms: (1) a directed Random Walk method for discrete space and time, and (2) a truncated Brownian Bridges method for continuous space and time. We illustrate these methods and demonstrate the effect of prism and mobility parameters on the visit probability distributions within the prism.

Predict the location of moving objects using mining association rules of movement patterns

A location-based service is an information and entertainmentservice, accessible with mobile devices through the mobile network andutilizing the ability to make use of the geographical position of the mobile device. Location-Based Services report the current location of the user. In order to support context-prediction and proactive devices, location-based services must be able to predict locations. In the world, there are many research issues of predicting the location of the moving objects. Some issuestowards studying fuzzy logic, statistical probability or combining different methods. The goal of this paper is to introduce a framework for pattern extraction and modeling, which helps todesign model for moving object databases. This paper also proposes a method to predict the location of moving objects using mining association rules of movement patterns. The experiments are given to show that the proposed method is more accurate when used in combination with the motion function prediction method.

Spatio-Temporal Queries for Moving Objects Data Warehousing

In the last decade, Moving Object Databases (MODs) have attracted a lot of attention from researchers. Several research works were conducted to extend traditional database techniques to accommodate the new requirements imposed by the continuous change in location information of moving objects. Managing, querying, storing, and mining moving objects were the key research directions. This extensive interest in moving objects is a natural consequence of the recent ubiquitous location-aware devices, such as PDAs, mobile phones, etc., as well as the variety of information that can be extracted from such new databases. In this paper we propose a Spatio-Temporal data warehousing (STDW) for efficiently querying location information of moving objects. The proposed schema introduces new measures like direction majority and other direction-based measures that enhance the decision making based on location information.

A High Performance Scalable Data Collection System for Moving Objects

help Locations Based Services systems to meet market demands, a performance and scalable collection framework was provided to gather different kinds of geographical data (from GPS devices, RFID, Data base transactions, etc.) based on the unified moving object trajectories' Meta-model. Basically, the collection framework offers components to collect spatio-temporal data from GPS enabling devices using .Net sockets, and benefits from executing all code in .NET Common Language Runtime to increase the system performance compared to unmanaged codes that decrease performance because of additional requirement security checks. In order to test the scalability of the proposed collection system, a vehicle tracking simulator was developed to generate and simulate tremendous spatio-temporal data of different moving objects. The main goal of this manuscript is to illustrate the importance of the collection framework and analyze the worst-case testing. Following this strategy, monitoring how scalable the proposed data collection framework is possible when dealing with very large and simultaneous moving objects trajectories datasets in real time. Keywordsdata modeling, Trajectory data collection framework, moving object database, space time path, spatial data engineering, trajectory meta-model.

Concealing Sequential and Spatiotemporal patterns using Polynomial Sanitization

Process of relevant pattern observation which is present in the database observed as a hurdle for database protection. Over the time, various approaches for hiding knowledge have emerged, mainly in the focus of Association rules and frequent item sets mining. This paper, have seen the problem in different view i.e., Knowledge hiding to the context where the data and extracted knowledge have a sequential structure. The concept of NP hardness is observed over the sequential pattern hiding. A polynomial sanitization algorithm was adopted and implemented over the spatiotemporal patterns extracted from moving objects databases. Disseminating datasets of this kind presents a considerable opportunity for knowledge patterns of interest. The developed model is kept under the attack, which exploits the knowledge of underlying road networks.

Map-matched trajectory compression

The wide usage of location aware devices, such as GPS-enabled cellphones or PDAs, generates vast volumes of spatiotemporal streams of location data raising management challenges, such as efficient storage and querying. Therefore, compression techniques are inevitable also in the field of moving object databases. Related work is relatively limited and mainly driven by line simplification and data sequence compression techniques. Moreover, due to the (unavoidable) erroneous measurements from GPS devices, the problem of matching the location recordings with the underlying traffic network has recently gained the attention of the research community. So far, the proposed compression techniques have not been designed for network constrained moving objects, while on the other hand, existing map matching algorithms do not take compression aspects into consideration. In this paper, we propose solutions tackling the combined, map matched trajectory compression problem, the efficiency of which is demonstrated through an extensive experimental evaluation on offline and online trajectory data using synthetic and real trajectory datasets.

Decentralized Monitoring of Moving Objects in a Transportation Network Augmented with Checkpoints

This paper examines efficient and decentralized monitoring of objects moving in a transportation network. Previous work in moving object monitoring has focused primarily on centralized information systems, like moving object databases and geographic information systems. In contrast, in this paper monitoring is in-network, requiring no centralized control and allowing for substantial spatial constraints to the movement of information. The transportation network is assumed to be augmented with fixed checkpoints that can detect passing mobile objects. This assumption is motivated by many practical applications, from traffic management in vehicle ad hoc networks to habitat monitoring by tracking animal movements. In this context, this paper proposes and evaluates a family of efficient decentralized algorithms for capturing, storing and querying the movements of objects. The algorithms differ in the restrictions they make on the communication and sensing constraints to the mobile nodes and the fixed checkpoints. The performance of the algorithms is evaluated and compared with respect to their scalability (in terms of communication and space complexity), and their latency (the time between when a movement event occurs, and when all interested nodes are updated with records about that event). The conclusions identify three key principles for efficient decentralized monitoring of objects moving past checkpoints: structuring computation around neighboring checkpoints; taking advantage of mobility diffusion and separating the generation and querying of movement information.

Indexing moving objects for directions and velocities queries

Moving object databases are required to support different types of queries with a large number of moving objects. New types of queries namely directions and velocity queries (DV queries), are to be supported and covered. The TPR-tree and its successors are efficient indexes that support spatio-temporal queries for moving objects. However, neither of them support the new DV queries. In this paper, we propose a new index for moving objects based on the TPR*-tree, named Direction and Velocity of TPR*-tree or DV-TPR*-tree, in order to build data a structure based on the spatial, direction and velocity domains. DV-TPR*-tree obtains an ideal distribution that supports and fulfils the new query types (DV queries). Extensive performance studies show that the query performance of DV-TPR*-tree outperforms the TPR-tree and its successors.

Segmentation and Sampling of Moving Object Trajectories Based on Representativeness

Moving Object Databases (MOD), although ubiquitous, still call for methods that will be able to understand, search, analyze, and browse their spatiotemporal content. In this paper, we propose a method for trajectory segmentation and sampling based on the representativeness of the (sub)trajectories in the MOD. In order to find the most representative subtrajectories, the following methodology is proposed. First, a novel global voting algorithm is performed, based on local density and trajectory similarity information. This method is applied for each segment of the trajectory, forming a local trajectory descriptor that represents line segment representativeness. The sequence of this descriptor over a trajectory gives the voting signal of the trajectory, where high values correspond to the most representative parts. Then, a novel segmentation algorithm is applied on this signal that automatically estimates the number of partitions and the partition borders, identifying homogenous partitions concerning their representativeness. Finally, a sampling method over the resulting segments yields the most representative subtrajectories in the MOD. Our experimental results in synthetic and real MOD verify the effectiveness of the proposed scheme, also in comparison with other sampling techniques.

A generic data model for moving objects

Moving objects databases should be able to manage trips that pass through several real world environments, e.g., road network, indoor. However, the current data models only deal with the movement in one situation and cannot represent comprehensive trips for humans who can move inside a building, walk on the pavement, drive on the road, take the public vehicles (bus or train), etc. As a result, existing queries are solely limited to one environment. In this paper, we design a data model that is able to represent moving objects in multiple environments in order to support novel queries on trips in different surroundings and various transportation modes (e.g., Car, Walk, Bus). A generic and precise location representation is proposed that can apply in all environments. The idea is to let the space for moving objects be covered by a set of so-called infrastructures each of which corresponds to an environment and defines the available places for moving objects. Then, the location is represented by referencing to the infrastructure. We formulate the concept of space and infrastructure and propose the methodology to represent moving objects in different environments with the integration of precise transportation modes. Due to different infrastructure characteristics, a set of novel data types is defined to represent infrastructure components. To efficiently support new queries, we design a group of operators to access the data. We present how such a data model is implemented in a database system and report the experimental results. The new model is designed with attention to the data models of previous work for free space and road networks to have a consistent type system and framework of operators. In this way, a powerful set of generic query operations is available for querying, together with those dealing with infrastructures and transportation modes. We demonstrate these capabilities by formulating a set of sophisticated queries across all infrastructures.

A delineated R-tree indexing method for managing moving objects

Recent technological advances in location aware, mobile and wireless devices have promoted the demand for efficient indexing structures for retrieval of information from moving objects databases. However, using existing indexing methods on data that are changing frequently will lead to serious performance overhead due to the fact that the index structure should be updated constantly which is clearly not an appropriate solution. Therefore, new methods must be utilised for moving object databases to address the indexing issue and as a result enhance the retrieval of information from these databases. This paper introduces delineated R-tree (DR-tree) indexing structure which has performance advantages over recent R-tree-based indexing structures for a number of essential operations such as read, write, node-split, and tree adjustment. DR-tree is a balanced tree, and the nodes common property is based on the principle of recursive decomposition of space without any overlap.